Machine learning approaches to estimate simulated cardiac ejection fraction from electrical impedance tomography

Abstract

The ejection fraction (EF) is a parameter that represents the amount of blood pumped out of each ventricle in each cardiac cycle and can be used for analyzing the heart failure. There are several diagnostic tests to determine whether the person has heart failure but some are expensive tests and they do not allow obtaining continuous estimations of EF. However, use the Electrical Impedance Tomography (EIT) with Regression Models is an alternative to obtain continuous estimations of EF. The quality of EIT is that it allows a quick diagnosis about the heart’s health, combining low cost and high portability. This paper it proposed four regression models, using the electrical measures from EIT, to estimate the EF: Gaussian Processes (GP), Support Vector Regression (SVR), Elastic Net Regression (ENR) and Multivariate Adaptive Regression Splines (MARS). The overall evaluation of results show that all models achieved competitive results and the method SVR has produced better results than the others tested.